Besides SiLRTC and HaLRTC, DFTS2 also includes ALTeC [1]}, CALTeC [2]}, and Navier-Stokes inpainting [3]}. All these methods exploit spatial structure of feature tensor channels to recover missing data. ALTeC assumes a linear relationship between a packet of feature data and its spatial neighbors, as well as collocated data in other feature channels. The coefficients of this linear relationship are obtained by pre-training. Meanwhile, for each missing feature packet, CALTeC finds the most correlated feature channel where collocated data is available, and then derives an affine transformation to fill in the missing data. Navier-Stokes inpainting is based on a “surface flow” of available spatially neighboring data into the missing region.
